Cross-testing of direct-action antivirals, universal vaccines, or search for host-level antivirals: what will sooner lead to a generic capability to combat the emerging viral pandemics?

INTRODUCTION: Mitigation of future viral pandemics is an enormous technical problem, but its solution is essential for preservation of life, economic well-being, and social stability. The author examined host-level, direct action antiviral, and universal vaccine approaches while presenting a specific screening proposal. AREAS COVERED: The author examined the most recent biomedical literature publicly available in the databases and identified the publications supporting the principle of cross-applicability of direct-action antivirals (DAA) within similar viral families and at greater phylogenetic distances. EXPERT OPINION: Comparing different approaches, the author showed that the cocktails of DAAs, including parent compounds that passed Phase I trials need to be preemptively tested for all major viral families, approved, and stockpiled (or dual-use production facilities designated). The quick distribution of the pre-approved and pre-positioned antiviral cocktails (even of moderate efficiency) reduces mortality and economic damage many-fold, resulting in the trillion-scale savings in a pandemic context. This pre-positioning approach is only one in the combinatorial toolkit that needs to be included in the plan for all viral families of importance. A dedicated international public-private initiative can achieve savings in these proactive preparedness efforts, as well as to keep the focus of politicians and public on the problem.

A public-private partnership for the express development of antiviral leads: a perspective view.

INTRODUCTION: The COVID-19 pandemic raises the question of strategic readiness for emergent pathogens. The current case illustrates that the cost of inaction can be higher in the future. The perspective article proposes a dedicated, government-sponsored agency developing anti-viral leads against all potentially dangerous pathogen species. AREAS COVERED: The author explores the methods of computational drug screening and in-silico synthesis and proposes a specialized government-sponsored agency focusing on leads and functioning in collaboration with a network of labs, pharma, biotech firms, and academia, in order to test each lead against multiple viral species. The agency will employ artificial intelligence and machine learning tools to cut the costs further. The algorithms are expected to receive continuous feedback from the network of partners conducting the tests. EXPERT OPINION: The author proposes a bionic principle, emulating antibody response by producing a combinatorial diversity of high q uality generic antiviral leads, suitable for multiple potentially emerging species. The availability of multiple pre-tested agents and an even greater number of combinations would reduce the impact of the next outbreak. The methodologies developed in this effort are likely to find utility in the design of chronic disease therapeutics.

Increased lifespan, decreased mortality, and delayed cognitive decline in osteoarthritis.

In absence of therapies targeting symptomatic dementia, better understanding of the biology underlying a cognitive decline is warranted. Here we present the results of a meta-analysis of the impact of osteoarthritis (OA) on cognitive decline and overall mortality. Across 7 independent datasets obtained in studies of populations in the USA, EU and Australia (NBER, NSHAP, TILDA, NACC, Kaiser Permanente, GRIM BOOKS, OAI, with a total of >7 × 107 profiles), OA cohorts demonstrated higher cognitive scores, later dementia onset as well as longer lifespan and lower age-specific all-cause mortality. Moreover, generalized OA with multiple localizations is associated with more significant reduction of mortality and dementia than a singly localized OA or no arthritis. In OA patients with younger ages, all-cause mortality was disproportionally reduced as compared to that in controls, while exponential term of Gompert'z hazard function was increased, accelerating mortality accrual at later ages. Up to 8-10% of poly-osteoarthritic patients are predicted and observed to reach centenarian lifespan, while in matched non-OA population the same benchmark is reached by less than 1% of patients. These results point at a possibility of life-extending and cognition preserving impacts of OA-conditioned immune system.

Pharmacological signatures of the reduced incidence and the progression of cognitive decline in ageing populations suggest the protective role of beneficial polypharmacy.

Preventive treatments for dementia are warranted. Here we show that utilization of certain combinations of prescription medications and supplements correlates with reduced rates of cognitive decline. More than 1,900 FDA-approved agents and supplements were collapsed into 53 mechanism-based groups and traced in electronic medical records (EMRs) for >50,000 patients. These mechanistic groups were aligned with the data presented in more than 300 clinical trials, then regression model was built to fit the signals from EMRs to clinical trial performance. While EMR signals of each single agents correlated with clinical performance relatively weakly, the signals produced by combinations of active compounds were highly correlated with the clinical trial performance (R = 0.93, p = 3.8 x10^-8). Higher ranking pharmacological modalities were traced in patient profiles as their combinations, producing protective complexity estimates reflecting degrees of exposure to beneficial polypharmacy. For each age strata, the higher was the protective complexity score, the lower was the prevalence of dementia, with maximized life-long effects for the highest regression score /diversity compositions. The connection was less strong in individuals already diagnosed with cognitive impairment. Confounder analysis confirmed an independent effect of protective complexity in multivariate context. A sub-cohort with lifelong odds of dementia decreased > 5-folds was identified; this sub-cohort should be studied in further details, including controlled clinical trials. In short, our study systematically explored combinatorial preventive treatment regimens for age-associated multi-morbidity, with an emphasis on neurodegeneration, and provided extensive evidence for their feasibility.

Knowledge-Based Compact Disease Models: A Rapid Path from High-Throughput Data to Understanding Causative Mechanisms for a Complex Disease.

High-throughput profiling of human tissues typically yields the gene lists composed of a variety of more or less relevant molecular entities. These lists are riddle by false positive observations that often obstruct generation of mechanistic hypothesis that may explain complex phenotype. From general probabilistic considerations, the gene lists enriched by the mechanistically relevant targets can be far more useful for subsequent experimental design or data interpretation. Using Alzheimer's disease as example, the candidate gene lists were processed into different tiers of evidence consistency established by enrichment analysis across subdatasets collected within the same experiment and across different experiments and platforms. The cutoffs were established empirically through ontological and semantic enrichment; resultant shortened gene list was reexpanded by Ingenuity Pathway Assistant tool. The resulting subnetworks provided the basis for generating mechanistic hypotheses that were partially validated by mined experimental evidence. This approach differs from previous consistency-based studies in that the cutoff on the Receiver Operating Characteristic of the true-false separation process is optimized by flexible selection of the consistency building procedure. The resultant Compact Disease Models (CDM) composed of the gene list distilled by this analytic technique and its network-based representation allowed us to highlight possible role of the protein traffic vesicles in the pathogenesis of Alzheimer's. Considering the distances and complexity of protein trafficking in neurons, it is plausible to hypothesize that spontaneous protein misfolding along with a shortage of growth stimulation may provide a shortcut to neurodegeneration. Several potentially overlapping scenarios of early-stage Alzheimer pathogenesis are discussed, with an emphasis on the protective effects of Angiotensin receptor 1 (AT-1) mediated antihypertensive response on cytoskeleton remodeling, along with neuronal activation of oncogenes, luteinizing hormone signaling and insulin-related growth regulation, forming a pleiotropic model of its early stages. Compact Disease Model generation is a flexible approach for high-throughput data analysis that allows extraction of meaningful, mechanism-centered gene sets compatible with instant translation of the results into testable hypotheses.

Predicting High-Impact Pharmacological Targets by Integrating Transcriptome and Text-Mining Features.

PURPOSE: Novel, "outside of the box" approaches are needed for evaluating candidate molecules, especially in oncology. Throughout the years of 2000-2010, the efficiency of drug development fell to barely acceptable levels, and in the second decade of this century, levels have improved only marginally. This dismal condition continues despite unprecedented progress in the development of a variety of high-throughput tools, computational methods, aggregated databases, drug repurposing programs and innovative chemistries. Here we tested a hypothesis that the economic impact of targeting a particular gene product is predictable a priori by employing a combination of transcriptome profiles and quantitative metrics reflecting existing literature. METHODS: To extract classification features, the gene expression patterns of a posteriori high-impact and low-impact anti-cancer target sets were compared. To minimize the possible bias of text-mining, the number of manuscripts published prior to the first clinical trial or relevant review paper, as well as its first derivative in this interval, were collected and used as quantitative metrics of public interest. RESULTS: By combining the gene expression and literature mining features, a 4-fold enrichment in high-impact targets was produced, resulting in a favourable ROC curve analysis for the top impact targets. The dataset was enriched by the highest impact anti-cancer targets, while demonstrating drastic differences in economic value between high and low-impact targets. Known anti-cancer products of EGFR, ERBB2, CYP19A1/aromatase, MTOR, PTGS2, tubulin, VEGFA, BRAF, PGR, PDGFRA, SRC, REN, CSF1R, CTLA4 and HSP90AA1 genes received the highest scores for predicted impact, while microsomal steroid sulfatase, anticoagulant protein C, p53, CDKN2A, c-Jun, and TNSFS11 were highlighted as most promising research-stage targets. CONCLUSIONS: A significant cost reduction may be achieved by a priori impact assessment of targets and ligands before their development or repurposing. Expanding a suite of combinational treatments could also decrease the costs, while achieving a higher impact per developed ligand. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

Knowledge-based compact disease models identify new molecular players contributing to early-stage Alzheimer's disease.

BACKGROUND: High-throughput profiling of human tissues typically yield as results the gene lists comprised of a mix of relevant molecular entities with multiple false positives that obstruct the translation of such results into mechanistic hypotheses. From general probabilistic considerations, gene lists distilled for the mechanistically relevant components can be far more useful for subsequent experimental design or data interpretation. RESULTS: The input candidate gene lists were processed into different tiers of evidence consistency established by enrichment analysis across subsets of the same experiments and across different experiments and platforms. The cut-offs were established empirically through ontological and semantic enrichment; resultant shortened gene list was re-expanded by Ingenuity Pathway Assistant tool. The resulting sub-networks provided the basis for generating mechanistic hypotheses that were partially validated by literature search. This approach differs from previous consistency-based studies in that the cut-off on the Receiver Operating Characteristic of the true-false separation process is optimized by flexible selection of the consistency building procedure. The gene list distilled by this analytic technique and its network representation were termed Compact Disease Model (CDM). Here we present the CDM signature for the study of early-stage Alzheimer's disease. The integrated analysis of this gene signature allowed us to identify the protein traffic vesicles as prominent players in the pathogenesis of Alzheimer's. Considering the distances and complexity of protein trafficking in neurons, it is plausible that spontaneous protein misfolding along with a shortage of growth stimulation result in neurodegeneration. Several potentially overlapping scenarios of early-stage Alzheimer pathogenesis have been discussed, with an emphasis on the protective effects of AT-1 mediated antihypertensive response on cytoskeleton remodeling, along with neuronal activation of oncogenes, luteinizing hormone signaling and insulin-related growth regulation, forming a pleiotropic model of its early stages. Alignment with emerging literature confirmed many predictions derived from early-stage Alzheimer's disease' CDM. CONCLUSIONS: A flexible approach for high-throughput data analysis, the Compact Disease Model generation, allows extraction of meaningful, mechanism-centered gene sets compatible with instant translation of the results into testable hypotheses.

Patent landscape of countermeasures against smallpox and estimation of grant attraction capability through patent landscape data.

The study was concerned with countermeasures against a possible smallpox outbreak. In the process of assessment 18 landscaping sectors were defined and described, the advantages and drawbacks of the corresponding countermeasures being reviewed. The data of the previously published influenza landscape were revisited. The current economic climate of deficit cutting (austerity) also puts emphasis on the optimization of capital investment. We used the materials of the landscape to define and analyze metrics of capital placement optimization. Value score was obtained by fitting patent landscape internals to the sale price of individual patents. Success score was obtained as a product of a-priori parameters that measure likelihood of emergence of a marketable product in a technological sector. Both scores were combined in a qualitative metric. Our methodology defined weight as a product of the sector size by the success score. We hypothesized - based on the material of two landscapes- that a life cycle of a technology begins in IP space with a high patent quality low volume "bud" of low weight, reaches maximum weight and then weight falls again when the technology becomes outdated. The weight and the annual dynamic of weight can serve a measure of investment risk and return. In this report we modeled investment by issue of government grants or purchase of patents by government. In the smallpox landscape the number of patents purchased by government agencies was the highest in the sectors with the highest weight and the trend was confirmed by the count of NIH grants issued in support of the technological sectors. In the influenza landscape only grant issue count was statistically meaningful and the trend was also confirmed. To better fit the grant support levels, the weight expression was optimized by using training coefficients. We propose to use value scores for evaluation of individual patent publications/company portfolios and to use weights for assessment of technological sectors. Such a combination of automated analytical tools may lead to optimized allocation of capital and is intended to support the decisions taken by human experts.

Influenza antiviral therapeutics.

In this review we conducted a landscaping study of the therapeutic anti-influenza agents, limiting the scope by exclusion of vaccines. The resulting 2800 patent publications were classified into 23 distinct technological sectors. The mechanism of action, the promise and drawbacks of the corresponding technological sectors were explored on comparative basis. A set of quantitative parameters was defined based on landscaping procedure that appears to correlate with the practical success of a given class of therapeutics. Thus, the sectors not considered promising from the mechanistic side were also displaying low value of the classifying parameters. The parameters were combined into a probabilistic Marketing Prediction Score, assessing a likelihood of a given sector to produce a marketable product. The proposed analytical methodology may be useful for automatic search and assessment of technologies for the goals of acquisition, investment and competitive bidding. While not being a substitute for an expert evaluation, it provides an initial assessment suitable for implementation with large-scale automated landscaping.

Expression variation: its relevance to emergence of chronic disease and to therapy.

BACKGROUND: Stochastic fluctuations in the protein turnover underlie the random emergence of neural precursor cells from initially homogenous cell population. If stochastic alteration of the levels in signal transduction networks is sufficient to spontaneously alter a phenotype, can it cause a sporadic chronic disease as well -- including cancer? METHODS: Expression in >80 disease-free tissue environments was measured using Affymetrix microarray platform comprising 54675 probe-sets. Steps were taken to suppress the technical noise inherent to microarray experiment. Next, the integrated expression and expression variability data were aligned with the mechanistic data covering major human chronic diseases. RESULTS: Measured as class average, variability of expression of disease associated genes measured in health was higher than variability of random genes for all chronic pathologies. Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes. Same held for magnitude of gene expression. The genes known to participate in multiple chronic disorders demonstrated the highest variability. Disease-related gene categories displayed on average more intricate regulation of biological function vs random reference, were enriched in adaptive and transient functions as well as positive feedback relationships. CONCLUSIONS: A possible causative link can be suggested between normal (healthy) state gene expression variation and inception of major human pathologies, including cancer. Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization. The results of the study suggest the need to advance personalized therapy development.

Successful anti-cancer drug targets able to pass FDA review demonstrate the identifiable signature distinct from the signatures of random genes and initially proposed targets.

MOTIVATION: New efforts to guide and prioritize the selection of cancer drug targets are urgently needed, as is evident by the slow development of novel anti-cancer agents and the narrow therapeutic index of existing drugs. Given these limitations, the current study was conducted to explore the classification features defining the therapeutic success that can result from targeting a particular gene. RESULTS: Classification was based on extracting features specific to known successful anti-cancer targets and combining them in a linear classifier, resulting in calculation of an enrichment score for each gene. Extended description, the search tool used in this study, enriched existing drug target candidates by up to 10-fold at an approximately 50% recall rate, covering approximately 24 000 genes or approximately 80% of genome. More importantly, the target category with high attrition rate was classified from target category with low attrition rate, allowing to refine the drug development portfolios. Biological relevance of the parameters comprising the enrichment score was explored. Enrichment in cancer-specific effects was independently demonstrated by literature analysis. Imposing these enrichment scores on existing structural, pathway and phenotype-based procedures for prospective target selection may enhance the efficiency and accuracy of target identification and accelerate drug design. AVAILABILITY: The software used in this work is available upon request.

Ingenuity network-assisted transcription profiling: Identification of a new pharmacologic mechanism for MK886.

The small molecular inhibitor MK886 is known to block 5-lipoxygenase-activating protein ALOX5AP and shows antitumor activity in multiple human cell lines. The broad antitumor therapeutic window reported in vivo for MK886 in rodents supports further consideration of this structural class. Better understanding of the mode of action of the drug is important for application in humans to take place. Affymetrix microarray study was conducted to explore MK886 pharmacologic mechanism. Ingenuity Pathway Analysis software was applied to validate the results at the transcriptional level by putting them in the context of an experimental proteomic network. Genes most affected by MK886 included actin B and focal adhesion components. A subsequent National Cancer Institute-60 panel study, RT-PCR validation followed by confocal microscopy, and Western blotting also pointed to actin B down-regulation, filamentous actin loss, and disorganization of the transcription machinery. In agreement with these observations, MK886 was found to enhance the effect of UV radiation in H720 lung cancer cell line. In light of the modification of cytoskeleton and cell motility by lipid phosphoinositide 3-kinase products, MK886 interaction with actin B might be biologically important. The low toxicity of MK886 in vivo was modeled and explained by binding and transport by dietary lipids. The rate of lipid absorbance is generally higher for tumors, suggesting a promise of a targeted liposome-based delivery system for this drug. These results suggest a novel antitumor pharmacologic mechanism.

Inhibitors of the arachidonic acid pathway and peroxisome proliferator-activated receptor ligands have superadditive effects on lung cancer growth inhibition.

Arachidonic acid (AA) metabolizing enzymes and peroxisome proliferator-activated receptors (PPARs) have been shown to regulate the growth of epithelial cells. We have previously reported that exposure to the 5-lipoxygenase activating protein-directed inhibitor MK886 but not the cyclooxygenase inhibitor, indomethacin, reduced growth, increased apoptosis, and up-regulated PPARalpha and gamma expression in breast cancer cell lines. In the present study, we explore approaches to maximizing the proapoptotic effects of PPARgamma on lung cancer cell lines. Non-small-cell cancer cell line A549 revealed dose-dependent PPARgamma reporter activity after treatment with MK886. The addition of indomethacin in combination with MK886 further increases reporter activity. We also show increased growth inhibition and up-regulation of apoptosis after exposure to MK886 alone, or in combination with indomethacin and the PPAR ligand, 15-deoxy-Delta12,14-prostaglandin J2 compared with single drug exposures on the adenocarcinoma cell line A549 and small-cell cancer cell lines H345, N417, and H510. Real-time PCR analyses showed increased PPAR mRNA and retinoid X receptor (RXR)alpha mRNA expression after exposure to MK886 and indomethacin in a time-dependent fashion. The results suggest that the principal proapoptotic effect of these drugs may be mediated through the known antiproliferative effects of the PPARgamma-RXR interaction. We therefore explored a three-drug approach to attempt to maximize this effect. The combination of low-dose MK886, ciglitazone, and 13-cis-retinoic acid interacted at least in a superadditive fashion to inhibit the growth of lung cancer cell lines A549 and H1299, suggesting that targeting PPARgamma and AA action is a promising approach to lung cancer growth with a favorable therapeutic index.

Mechanism and biological role of nitric oxide binding to cytochrome c'.

The binding of nitric oxide to ferric and ferrous Chromatium vinosum cytochrome c' was studied. The extinction coefficients for the ferric and ferrous nitric oxide complexes were measured. A binding model that included both a conformational change and dissociation of the dimer into subunits provided the best fit for the ferric cytochrome c' data. The NO (nitric oxide) binding affinity of the WT ferric form was found to be comparable to the affinities displayed by the ferric myoglobins and hemoglobins. Using an improved fitting model, positive cooperativity was found for the binding of NO to the WT ferric and ferrous forms, while anticooperativity was the case for the Y16F mutant. Structural explanations accounting for the binding are proposed. The NO affinity of ferrous cytochrome c' was found to be much lower than the affinities of myoglobins, hemoglobins, and pentacoordinate heme models. Structural factors accounting for the difference in affinities were analyzed. The NO affinity of ferrous cytochrome c' was found to be in the range typical of receptors and carriers. In addition, cytochrome c' was found to react with cytosolic light-irradiated membranes in the presence of succinate and carbon monoxide. With these results, a biochemical model of cytochrome c' functioning as a nitric oxide carrier was proposed.